Showing posts with label Data Structure. Show all posts
Showing posts with label Data Structure. Show all posts

Data Structures and Algorithms: Annotated Reference with Examples

By Granville Barnett and Luca Del Tongo

Every book has a story as to how it came about and this one is no different, although we would be lying if we said its development had not been somewhat impromptu. Put simply this book is the result of a series of emails sent back and forth between the two authors during the development of a library for the .NET framework of the same name (with the omission of the subtitle of course!). The conversation started of something like, Why don't we create a more aesthetically pleasing way to present our pseudocode?" After a few weeks this new presentation style had in fact grown into pseudocode listings with chunks of text describing how the data structure or algorithm in question works and various other things about it. At this point we thought, What the heck, let's make this thing into a book!" And so, in the summer of 2008 we began work on this book side by side with the actual library implementation. When we started writing this book the only things that we were sure about with respect to how the book should be structured were:
  1. always make explanations as simple as possible while maintaining a moderately fine degree of precision to keep the more eager minded reader happy; and
  2. inject diagrams to demystify problems that are even moderately challenging to visualize (. . . and so we could remember how our own algorithms worked when looking back at them); and finally
  3. present concise and self explanatory pseudo code listings that can be ported easily to most mainstream imperative programming languages like C++, C#, and Java.
A key factor of this book and its associated implementations is that all algorithms (unless otherwise stated) were designed by us, using the theory of the algorithm in question as a guideline (for which we are eternally grateful to their original creators). Therefore they may sometimes turn out to be worse than the "normal" implementations|and sometimes not. We are two fellows of the opinion that choice is a great thing. Read our book, read several others on the same subject and use what you see fit from each (if anything) when implementing your own version of the algorithms in question. Through this book we hope that you will see the absolute necessity of understanding which data structure or algorithm to use for a certain scenario. In all projects, especially those that are concerned with performance (here we apply an even greater emphasis on real-time systems) the selection of the wrong data structure or algorithm can be the cause of a great deal of performance pain
Therefore it is absolutely key that you think about the run time complexity and space requirements of your selected approach. In this book we only explain the theoretical implications to consider, but this is for a good reason: compilers are very different in how they work. One C++ compiler may have some amazing optimisation phases specically targeted at recursion, another may not, for example. Of course this is just an example but you would be surprised by how many subtle differences there are between compilers. These differences which may make a fast algorithm slow, and vice versa. We could also factor in the same concerns about languages that target virtual machines, leaving all the actual various implementation issues to you given that you will know your language's compiler much better than us...well in most cases. This has resulted in a more concise book that focuses on what we think are the key issues.
One final note: never take the words of others as gospel; verify all that can be feasibly veri¯ed and make up your own mind. We hope you enjoy reading this book as much as we have enjoyed writing it.
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Download free Data Structures and Algorithms ebooks

This posting provides you free Datastructures and algorithms ebook site links which teaches you to study datastructures through different languages, various alogrithms, complexities, alogrithms directories, graph theories, computer datastructures etc.
You can get free downloads in datastructures, alogrithms, etc. Following are the free ebooks dowloads written by different authors.
  1. Algorithmic Information Theory By G J Chaitin
  2. Algorithms and Complexity by Herbert S. Wilf
  3. Algorithms and Data Structures in VLSI Design By Christoph Meinel and Thorsten Theobald
  4. Algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani
  5. Algorithms for Modular Elliptic Curves By J. E. Cremona
  6. Algorithms for programmers By Jorg Arndt
  7. Art of Programming Contest - C Programming Tutorials Data Structures Algorithms
  8. Average Case Analysis of Algorithms on Sequences by Wojciech Szpankowski
  9. Combinatorial Algorithms By Jeff Erickson
  10. Computer Programming Algorithms Directory
  11. Data Structure in Java By Sandra Andersen
  12. Data Structures And Algorithms - CSEd Tools
  13. Data Structures and Algorithms By Alison Cawsey
  14. Data Structures and Algorithms By John Morris
  15. Data Structures and Algorithms in C++ By Michael T. Goodrich, Roberto Tamassia and David M. Mount
  16. Data Structures and Algorithms with Object-Oriented Design Patterns in C# By Bruno R.Preiss
  17. Data Structures and Algorithms with Object-Oriented Design Patterns in C++ By Bruno R. Preiss
  18. Data Structures and Algorithms with Object-Oriented Design Patterns in Java By Bruno R. Preiss
  19. Data Structures By Peter M Williams
  20. Data Structures:All Chapters From Wikibooks, the open-content textbooks collection
  21. Dictionary of Algorithms and Data Structures
  22. Efficient Algorithms for Sorting and Synchronization By Andrew Tridgell
  23. Foundations of Computer By Lawrence C Paulson
  24. Graph-Theoretic Algorithms By Therese Biedl
  25. Java Data Structures (2nd edition)
  26. Object Oriented Datastructures using Java By Nell Dale, Daniel T. Joyce and Chip Weems
  27. Planning Algorithms By Steven M. LaValle
  28. Problems on Algorithms By William Gasarch and Ian Parberry
  29. Sorting and Searching Algorithms: A Cookbook By Thomas Niemann
  30. The FXT library: Fast transforms and low level algorithms

Object Oriented Datastructures using Java

By Nell Dale, Daniel T. Joyce and Chip Weems
Welcome to the first edition of Object-Oriented Data Structures using Java. This book has been written to present the algorithmic, programming, and structuring techniques of a traditional data structures course in an objectoriented context. You’ll find that all of the familiar topics of lists, stacks, queues, trees, graphs, sorting, searching, Big-O complexity analysis, and recursion are still here, but covered from an object-oriented point of view using Java. Thus, our structures
are defined with Java interfaces and encapsulated as Java classes. We use abstract classes and inheritance, as appropriate, to take advantage of the relationships among various versions of the data structures. We use design aids, such as Class-Responsibility-Collaborator (CRC) Cards and Universal Modeling Language (UML) diagrams, to help us model and visualize our classes and their interrelationships. We hope that you enjoy this modern and up-to-date approach to the traditional data structures course.
Abstract Data Types

Over the last 16 years, the focus of the data structures course has broadened considerably. The topic of data structures now has been subsumed under the broader topic of abstract data types (ADTs)—the study of classes of objects whose logical behavior is defined by a set of values and a set of operations.
The term abstract data type describes a domain of values and set of operations that are specified independently of any particular implementation. The shift in emphasis is representative of the move towards more abstraction in computer science education. We now are interested in the study of the abstract properties of classes of data objects in addition to how the objects might be represented in a program.
The data abstraction approach leads us, throughout the book, to view our data structures from three different perspectives: their specification, their application, and their implementation. The specification describes the logical or abstract level. This level is concerned with what the operations are and what they do. The application level, sometimes called the user level, is concerned with how the data type might be used to solve a problem. This level is concerned with why the operations do what they do. The implementation level is where the operations are actually coded. This level is concerned with the how questions.
Using this approach, we stress computer science theory and software engineering principles, including modularization, data encapsulation, information hiding, data abstraction, stepwise refinement, visual aids, the analysis of algorithms, and software verification methods. We feel strongly that these principles should be introduced to computer science students early in their education so that they learn to practice good software techniques from the beginning.
An understanding of theoretical concepts helps students put the new ideas they encounter into place, and practical advice allows them to apply what they have learned. To teach these concepts we consistently use intuitive explanations, even for topics that have a basis in mathematics, like the analysis of algorithms. In all cases, our highest goal has been to make our explanations as readable and as easily understandable as possible.

Data Structures in Java - A Laboratory Course

By Sandra Andersen

To learn a subject such as computer science, you need to immerse yourself in it --learning by doing rather than by simply observing. Through the study of several classic data structures and algorithms, you will become a better informed and more knowledgeable computer science student and programmer. To be able to professionally choose the best algorithm and data structure for a particular set of resource constraints takes practice.

An emphasis on learning by doing is used throughout Data Structures in Java: A Laboratory Course. In each laboratory, you explore a particular data structure by implementing it. As you create an implementation, you learn how the data structure works and how it can be applied. The resulting implementation is a working piece of software that you can use in later laboratories and programming projects.

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Data Structures in Java: A Laboratory Course

Data Structures:All Chapters

From Wikibooks, the open-content textbooks collection
Computers can and usually store and process vast amounts of data. Some of this data is of use in limited ways (such as the company's name, the date, etc) while most application data follows some pattern. For example, student records at a university would typically contain an id-number, names, addresses, contact details, course information, etc. For conceptual and data processing efficiency reasons this vast amount of data should be structured. That is, the same sort of data, in our student records example, would be provided for each student. Within the one application there would typically be many of such data structures created. Also typically there would be some conceptual relationships which link data structures.
There are no hard and fast rules as to how data must be structured. The structures which are adopted will be determined by the purpose to be achieved. Sometimes the structure of the software may be a relevant consideration, as well as the amount of data involved.
The objective is, ultimately, to process the data captured or stored on the computer in the most efficient manner.
Formal data structures enable a programmer to mentally structure the large amounts of data into conceptually manageable relationships.
Sometimes we use data structures to allow us to do more: for example, to accomplish fast searching or sorting of data. Other times, we use data structures so that we can do less: for example, the concept of the stack is a limited form of a more general data structure. These limitations provide us with guarantees that allow us to reason about our programs more easily. Data structures also provide guarantees about algorithmic complexity — choosing an appropriate data structure for a job is crucial for writing good software.
Because data structures are higher-level abstractions, they present to us operations on groups of data, such as adding an item to a list, or looking up the highest-priority item in a queue. When a data structure provides operations, we can call the data structure an abstract data type (sometimes abbreviated as ADT). Abstract data types can minimize dependencies in your code, which is important when your code needs to be changed. Because you are abstracted away from lower-level details, some of the higher-level commonalities one data structure shares with a different data structure can be used to replace one with the other.
Our programming languages come equipped with a set of built-in types, such as integers and floating-point numbers, that allow us to work with data objects for which the machine's processor has native support. These built-in types are abstractions of what the processor actually provides because built-in types hide details both about their execution and limitations.

Java Data Structures (2nd edition)

© 1996-2001, Particle
Welcome to Java Data Structures (2nd edition). This document was created with an intent to show people how easy Java really is, and to clear up a few things I've missed in the previous release of the document.............
In contrast to what most people think about Java, it being a language with no pointers, data structures are quite easy to implement. In this section, I'll demonstrate few basic data structures. By learning how easy they are to implement in Java, you'll be able to write any implementation yourself.
I also think that this document is a pretty good introduction to Data Structures in general. All these concepts can be applied in any programming language............

Data Structures

By Peter M Williams
These notes provide an introduction to some of the most commonly occurring data structures. The language used is Java. The aim is not the greatest generality. The DataStructures package developed here is not as extensive as the Collections framework, first released with Java 1.2. For portable applications, you should use the Collections framework where possible.
The DataStructures package, however, includes graphs which are not currently in the Collections framework; and the greater simplicity of the DataStructures package makes it more suitable as a basis for learning about fundamental principles of data structures and algorithms.

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